Development and validation of an ensemble learning risk model for sepsis after abdominal surgery by Xin Shu, Yujie Li, Yiziting Zhu, Zhiyong Yang, Xiang Liu, Xiaoyan Hu, Chunyong Yang, Lei Zhao, Tao Zhu, Yuwen Chen, Bin Yi

“…Routine clinical variables were implemented for model development. The Boruta algorithm was applied for feature selection. Afterwards, ensemble learning and eight other conventional algorithms were used for model fitting and validation based on all features and selected features. …”
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Development and Validation of a Radiomics Nomogram Based on Magnetic Resonance Imaging and Clinicoradiological Factors to Predict HCC TACE Refractoriness by Dong Y, Hu J, Meng X, Yang B, Peng C, Zhao W

“…The optimal model was presented as a nomogram and verified through calibration and decision curve analyses.Results: In evaluating radiomics models for predicting TACE refractoriness in HCC, the LR-developed portal venous phase (VP) model achieved optimal single-sequence performance (training AUC: 0.896, 95% CI: 0.843– 0.941; validation: 0.853, 0.727– 0.965). …”
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Machine learning models for the prediction of preclinical coal workers’ pneumoconiosis: integrating CT radiomics and occupational health surveillance records by Yankun Ma, Fengtao Cui, Yulong Yao, Fuhai Shen, Hongyi Qin, Bing Li, Yan Wang

“…Among 5 machine learning algorithms evaluated, the Decision Tree-based multimodal model showed superior predictive capacity on the test set of 142 samples, with an AUC of 0.94 (95% CI 0.88–0.99), accuracy 0.95, specificity 1.00, and Youden's index 0.83. …”
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Real-Time Intelligent Recognition and Precise Drilling in Strongly Heterogeneous Formations Based on Multi-Parameter Logging While Drilling and Drilling Engineering by Aosai Zhao, Yang Yu, Bin Wang, Yewen Liu, Jingyue Liu, Xubiao Fu, Wenhao Zheng, Fei Tian

“…The K-means clustering algorithm is employed to extract the deep geo-engineering characteristics from multi-source LWD data, thereby constructing a lithology label library and categorizing the training and testing datasets. …”
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On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks by Eftychios Protopapadakis, Athanasios Voulodimos, Anastasios Doulamis

“…We propose and explore a variety of combinatory sampling approaches that are based on sparse representative instances selection (SMRS), OPTICS algorithm, k-means clustering algorithm, and random selection. …”
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Vote-Based: Ensemble Approach by Abdul Ahad Abro

“…In most cases, the ensemble learning algorithm yields better performance than ML algorithms. …”
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Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage by Lei Tang, Ye Li, Ji Zhang, Feng Zhang, Qiaoling Tang, Xiangbin Zhang, Sai Wang, Yupeng Zhang, Siyuan Ma, Ran Liu, Lei Chen, Junyi Ma, Xuelun Zou, Tianxing Yao, Rongmei Tang, Huifang Zhou, Lianxu Wu, Yexiang Yi, Yi Zeng, Duolao Wang, Le Zhang

“…Abstract Patients with intracerebral hemorrhage (ICH) are highly susceptible to sepsis. This study evaluates the efficacy of machine learning (ML) models in predicting sepsis risk in intensive care units (ICUs) patients with ICH. …”
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CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

“…Four mainstream machine learning algorithm training models, namely, support vector machine (SVM), k-nearest neighbour (kNN), light gradient boosting (LightGBM) and logistic regression (LR), were constructed to determine the best classifier model. …”
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Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer by Quan Yuan, Rongjie Ye, Yao Qian, Hao Yu, Yuexin Zhou, Xiaoqiao Cui, Feng Liu, Ming Niu

“…Owing to the particularity of the indicators, training and validation were conducted on real clinical data.…”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Compared to other optimization algorithms such as genetic algorithm (GA) and whale optimization algorithm (WOA), the BWO algorithm demonstrates significant per-formance, with faster running speed, stronger stability, and greater robustness. …”
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Physics-Informed Neural Network for Load Margin Assessment of Power Systems with Optimal Phasor Measurement Unit Placement by Murilo Eduardo Casteroba Bento

“…The IEEE 68-bus system and the Brazilian interconnected power system were chosen as the test systems to perform the case studies and evaluations. Three different metaheuristics called the Hiking Optimization Algorithm, Artificial Protozoa Optimizer, and Particle Swarm Optimization were applied and evaluated in the test system. …”
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Development and validation of a model for predicting in-hospital mortality in patients with sepsis-associated kidney injury receiving renal replacement therapy: a retrospective coh... by Caifeng Li, Ke Zhao, Qian Ren, Lin Chen, Ying Zhang, Guolin Wang, Keliang Xie

“…Finally, DCA was employed to evaluate the clinical utility of the prediction models. …”
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Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection by Onur Polat, Ali Ayid Ahmad, Saadin Oyucu, Enes Algul, Ferdi Dogan, Ahmet Aksoz

“…CICIoT 2023 is used as the dataset. ADAM optimization algorithm with cross-entropy loss is used to eliminate overfitting and training is performed. …”
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Deep learning approach for survival prediction for patients with synovial sarcoma by Ilkyu Han, June Hyuk Kim, Heeseol Park, Han-Soo Kim, Sung Wook Seo

“…We developed a novel deep-learning-based prediction algorithm for survival rates of synovial sarcoma patients. …”
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Enhancing CNN-based network intrusion detection through hyperparameter optimization by Antanios Kaissar, Ali Bou Nassif, Bassel Soudan, MohammadNoor Injadat

“…Furthermore, hyperparameter optimization significantly reduces training and testing times. The GWO-optimized model achieved a reduction of >11 % in training time and 6.14 % in testing time on the UNSW-NB15 dataset. …”
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On the principles of building a model of a specialist – a graduate of a pedagogical university by K. S. Kаtаеv, S. G. Kаtаеv, I. V. Kаmenskaya

“…The issue is particularly important for a teacher training institution, given the staff shortage in Russian schools and vocational education and training colleges. …”
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Electrocardiography Denoising via Sparse Dictionary Learning from Small Datasets by Steinbrinker Tabea, Spicher Nicolai

“…Hence, we propose and evaluate a lightweight algorithm for electrocardiography denoising via sparse dictionary learning, targeting two types of noise: baseline wander and muscle artifacts. …”
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Prediction of three-year all-cause mortality in patients with heart failure and atrial fibrillation using the CatBoost model by Jiacan Wu, Guanghong Tao, Siyuan Xie, Han Yang, Fenglin Qi, Naiyue Bao, Zhuo Li, Guanglei Chang, Hua Xiao

“…The cohort was randomly divided into training (70%) and test (30%) sets. Feature selection utilized the Boruta algorithm and least absolute shrinkage and selection operator regression. …”
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Development of model for identifying homologous recombination deficiency (HRD) status of ovarian cancer with deep learning on whole slide images by Ke Zhang, Youhui Qiu, Songwei Feng, Han Yin, Qi Liu, Yuxin Zhu, Haoyu Cui, Xiaoying Wei, Guoqing Wang, Xiangxue Wang, Yang Shen

“…The assessment of HRD status has the important significance for the formulation of treatment plans, efficacy evaluation, and prognosis prediction of patients with ovarian cancer. …”
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CC-Former: Urban Flood Mapping from InSAR Coherence with Vision Transformer: Libya and Storm Daniel as Test Case by T. Saleh, T. Saleh, T. Saleh, S. Holail, M. Al-Saad, F. Xu, M. Zahran, G.-S. Xia, G.-S. Xia

“…Additionally, we propose a coherence-based scaling (CoBS) module designed to focus on the acquired coherence features of urban flood classes and mitigate the imbalanced distribution of training classes. For qualitative and quantitative evaluation, the proposed CC-Former model was trained and validated using multi-temporal, dual-polarized Sentinel-1 SAR data to map the flood extent in Derna, Libya, following Tropical Storm Daniel in September 2023. …”
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